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99 lines
3.1 KiB
99 lines
3.1 KiB
9 months ago
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swarms
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pip install swarms
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swarms is the most pythonic way of writing cognitive systems. Leveraging pydantic models as output schemas combined with langchain in the backend allows for a seamless integration of llms into your apps. It utilizes OpenAI Functions or LlamaCpp grammars (json-schema-mode) for efficient structured output. In the backend it compiles the swarms syntax into langchain runnables so you can easily invoke, stream or batch process your pipelines.
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Open in GitHub Codespaces
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from pydantic import BaseModel, Field
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from swarms import Anthropic
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from swarms import Agent
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# Initialize the schema for the person's information
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class Schema(BaseModel):
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name: str = Field(..., title="Name of the person")
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agent: int = Field(..., title="Age of the person")
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is_student: bool = Field(..., title="Whether the person is a student")
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courses: list[str] = Field(
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..., title="List of courses the person is taking"
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)
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# Convert the schema to a JSON string
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tool_schema = Schema(
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name="Tool Name",
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agent=1,
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is_student=True,
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courses=["Course1", "Course2"],
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)
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# Define the task to generate a person's information
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task = "Generate a person's information based on the following schema:"
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# Initialize the agent
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agent = Agent(
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agent_name="Person Information Generator",
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system_prompt=(
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"Generate a person's information based on the following schema:"
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),
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# Set the tool schema to the JSON string -- this is the key difference
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tool_schema=tool_schema,
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llm=Anthropic(),
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max_loops=3,
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autosave=True,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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interactive=True,
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# Set the output type to the tool schema which is a BaseModel
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output_type=tool_schema, # or dict, or str
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metadata_output_type="json",
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# List of schemas that the agent can handle
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list_tool_schemas = [tool_schema],
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function_calling_format_type = "OpenAI",
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function_calling_type = "json" # or soon yaml
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)
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# Run the agent to generate the person's information
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generated_data = agent.run(task)
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# Print the generated data
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print(f"Generated data: {generated_data}")
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Features
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🐍 pythonic
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🔀 easy swap between openai or local models
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🔄 dynamic output types (pydantic models, or primitives)
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👁️ vision llm support
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🧠 langchain_core as backend
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📝 jinja templating for prompts
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🏗️ reliable structured output
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🔁 auto retry parsing
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🔧 langsmith support
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🔄 sync, async, streaming, parallel, fallbacks
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📦 gguf download from huggingface
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✅ type hints for all functions and mypy support
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🗣️ chat router component
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🧩 composable with langchain LCEL
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🛠️ easy error handling
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🚦 enums and literal support
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📐 custom parsing types
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Documentation
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Checkout the docs here 👈
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Also highly recommend to try and run the examples in the ./examples folder.
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Contribution
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You want to contribute? Thanks, that's great! For more information checkout the Contributing Guide. Please run the dev setup to get started:
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git clone https://github.com/kyegomez/swarms.git && cd swarms
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./dev_setup.sh
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About
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⛓️ build cognitive systems, pythonic
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